MNT make files private in sklearn.svm (scikit-learn#15165)

Author: NicolasHug

.gitignore

@@ -113,3 +113,10 @@ sklearn/cluster/spectral.py
 sklearn/mixture/base.py
 sklearn/mixture/gaussian_mixture.py
 sklearn/mixture/bayesian_mixture.py
+
+sklearn/svm/base.py
+sklearn/svm/classes.py
+sklearn/svm/bounds.py
+sklearn/svm/libsvm.py
+sklearn/svm/libsvm_sparse.py
+sklearn/svm/liblinear.py

doc/modules/computing.rst

@@ -319,7 +319,7 @@ Latency and throughput should (asymptotically) grow linearly with the number
 of support vectors in a SVC or SVR model. The kernel will also influence the
 latency as it is used to compute the projection of the input vector once per
 support vector. In the following graph the ``nu`` parameter of
-:class:`sklearn.svm.classes.NuSVR` was used to influence the number of
+:class:`sklearn.svm.NuSVR` was used to influence the number of
 support vectors.
 
 .. |nusvr_model_complexity| image::  ../auto_examples/applications/images/sphx_glr_plot_model_complexity_influence_002.png

examples/applications/plot_model_complexity_influence.py

@@ -29,7 +29,7 @@
 from sklearn import datasets
 from sklearn.utils import shuffle
 from sklearn.metrics import mean_squared_error
-from sklearn.svm.classes import NuSVR
+from sklearn.svm import NuSVR
 from sklearn.ensemble.gradient_boosting import GradientBoostingRegressor
 from sklearn.linear_model.stochastic_gradient import SGDClassifier
 from sklearn.metrics import hamming_loss

examples/applications/plot_prediction_latency.py

@@ -29,7 +29,7 @@
 from sklearn.ensemble.forest import RandomForestRegressor
 from sklearn.linear_model.ridge import Ridge
 from sklearn.linear_model.stochastic_gradient import SGDRegressor
-from sklearn.svm.classes import SVR
+from sklearn.svm import SVR
 from sklearn.utils import shuffle
 
 

sklearn/_build_utils/deprecated_modules.py

@@ -61,6 +61,14 @@
      'sklearn.mixture', 'GaussianMixture'),
     ('_bayesian_mixture', 'sklearn.mixture.bayesian_mixture',
      'sklearn.mixture', 'BayesianGaussianMixture'),
+
+    ('_base', 'sklearn.svm.base', 'sklearn.svm', 'BaseLibSVM'),
+    ('_bounds', 'sklearn.svm.bounds', 'sklearn.svm', 'l1_min_c'),
+    ('_classes', 'sklearn.svm.classes', 'sklearn.svm', 'SVR'),
+    ('_libsvm', 'sklearn.svm.libsvm', 'sklearn.svm', 'fit'),
+    ('_libsvm_sparse', 'sklearn.svm.libsvm_sparse', 'sklearn.svm',
+     'set_verbosity_wrap'),
+    ('_liblinear', 'sklearn.svm.liblinear', 'sklearn.svm', 'train_wrap'),
 ]
 
 _FILE_CONTENT_TEMPLATE = """

sklearn/linear_model/logistic.py

@@ -21,7 +21,7 @@
 from .base import LinearClassifierMixin, SparseCoefMixin, BaseEstimator
 from .sag import sag_solver
 from ..preprocessing import LabelEncoder, LabelBinarizer
-from ..svm.base import _fit_liblinear
+from ..svm._base import _fit_liblinear
 from ..utils import check_array, check_consistent_length, compute_class_weight
 from ..utils import check_random_state
 from ..utils.extmath import (log_logistic, safe_sparse_dot, softmax,

sklearn/svm/__init__.py

@@ -10,10 +10,9 @@
 #         of their respective owners.
 # License: BSD 3 clause (C) INRIA 2010
 
-from .classes import SVC, NuSVC, SVR, NuSVR, OneClassSVM, LinearSVC, \
+from ._classes import SVC, NuSVC, SVR, NuSVR, OneClassSVM, LinearSVC, \
         LinearSVR
-from .bounds import l1_min_c
-from . import libsvm, liblinear, libsvm_sparse
+from ._bounds import l1_min_c
 
 __all__ = ['LinearSVC',
            'LinearSVR',
@@ -22,7 +21,4 @@
            'OneClassSVM',
            'SVC',
            'SVR',
-           'l1_min_c',
-           'liblinear',
-           'libsvm',
-           'libsvm_sparse']
+           'l1_min_c']

sklearn/svm/base.py renamed to sklearn/svm/_base.py

@@ -3,8 +3,9 @@
 import warnings
 from abc import ABCMeta, abstractmethod
 
-from . import libsvm, liblinear
-from . import libsvm_sparse
+from . import _libsvm as libsvm
+from .import _liblinear as liblinear
+from . import _libsvm_sparse as libsvm_sparse
 from ..base import BaseEstimator, ClassifierMixin
 from ..preprocessing import LabelEncoder
 from ..utils.multiclass import _ovr_decision_function

sklearn/svm/bounds.py renamed to sklearn/svm/_bounds.py

@@ -1,7 +1,7 @@
 import warnings
 import numpy as np
 
-from .base import _fit_liblinear, BaseSVC, BaseLibSVM
+from ._base import _fit_liblinear, BaseSVC, BaseLibSVM
 from ..base import BaseEstimator, RegressorMixin, OutlierMixin
 from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, \
     LinearModel